Simulation of multi-agent manufacturing systems using Agent-Based Modelling platforms

Multi-agent systems (MAS) are driving the way to design and engineer control solutions that exhibit flexibility, adaptation and reconfigurability, which are important advantages over traditional centralized systems. The understanding, design and testing of such distributed agent-based approaches, and particularly those exhibiting self-∗properties, are usually a hard task. Simulation assumes a crucial role to analyse the behaviour of MAS solutions during the design phase and before its deployment into the real operation. Particularly, Agent-Based Modelling (ABM) tools are well suited to simulate MAS systems that exhibit complex phenomena, like emergent behaviour and self-organization. This paper discusses the simulation of agent-based manufacturing systems and introduces the advantages of using ABM tools. The NetLogo platform is used to illustrate the benefits of such tools in the manufacturing world on the specification of a MAS system for a washing machine production line.

[1]  Vladimír Marík,et al.  Industrial adoption of agent-based technologies , 2005, IEEE Intelligent Systems.

[2]  Majid Hashemipour,et al.  Applications of Virtual Reality in Design and Simulation of Holonic Manufacturing Systems: A Demonstration in Die-Casting Industry , 2007, HoloMAS.

[3]  Paulo Leitão,et al.  Engineering tools for the integration of service-oriented production systems , 2009 .

[4]  Y. Shoham.  Introduction to Multi-Agent Systems , 2002 .

[5]  Vladimír Marík,et al.  Capabilities of Dynamic Reconfiguration of Multiagent-Based Industrial Control Systems , 2010, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[6]  Matthew J. Berryman,et al.  Review of Software Platforms for Agent Based Models , 2008 .

[7]  Robert Tobias,et al.  Evaluation of free Java-libraries for social-scientific agent based simulation , 2004, J. Artif. Soc. Soc. Simul..

[8]  Pavel Vrba,et al.  Simulation in agent-based control systems: MAST case study , 2006, Int. J. Manuf. Technol. Manag..

[9]  H. Van Dyke Parunak,et al.  Applications of distributed artificial intelligence in industry , 1996 .

[10]  Steven L. Lytinen,et al.  Agent-based Simulation Platforms: Review and Development Recommendations , 2006, Simul..

[11]  Cje Castle,et al.  Ucl Centre for Advanced Spatial Analysis Principles and Concepts of Agent-based Modelling for Developing Geospatial Simulations Principles and Concepts of Agent-based Modelling for Developing Geospatial Simulations 1.2.4.2: Guidelines for Choosing a Simulation / Modelling System .................24 , 2022 .

[12]  Nicholas R. Jennings,et al.  Foundations of distributed artificial intelligence , 1996, Sixth-generation computer technology series.

[13]  L. J. De Vin,et al.  Why we need to offer a modeling and simulation engineering curriculum , 2001, Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304).

[14]  W. Feurzeig,et al.  Programming-languages as a conceptual framework for teaching mathematics , 1969, SCOU.

[15]  Nelson Minar,et al.  The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations , 1996 .

[16]  Damien Trentesaux,et al.  A stigmergic approach for dynamic routing of active products in FMS , 2009, Comput. Ind..

[17]  Michael J. North,et al.  A Declarative Model Assembly Infrastructure for Verification and Validation , 2006, WCSS.

[18]  Mats Jägstam,et al.  Why we need to offer a modeling and simulation engineering curriculum , 2001, WSC '01.

[19]  Sean Luke,et al.  MASON: A Multiagent Simulation Environment , 2005, Simul..

[20]  Danny Weyns,et al.  Multi-Agent Systems , 2009 .